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    National Tsing Hua University Institutional Repository > 科技管理學院  > 計量財務金融學系 > 期刊論文 >  Sample means, sample autocovariances, and linear regression of stationary multivariate long memory processes


    Please use this identifier to cite or link to this item: http://nthur.lib.nthu.edu.tw/dspace/handle/987654321/62092


    Title: Sample means, sample autocovariances, and linear regression of stationary multivariate long memory processes
    Authors: Chung CF
    教師: 鐘經樊
    Date: 2002
    Publisher: CAMBRIDGE UNIV PRESS
    Relation: Econometric Theory, CAMBRIDGE UNIV PRESS,18, 51-78,2002
    Keywords: NOISE
    VARIABLES
    AUTOCORRELATIONS
    MODELS
    QUADRATIC-FORMS
    INTEGRATED PROCESSES
    TIME-SERIES REGRESSION
    RANGE DEPENDENT ERRORS
    MAXIMUM-LIKELIHOOD-ESTIMATION
    NONCENTRAL LIMIT-THEOREMS
    Abstract: We develop an asymptotic theory for the first two sample moments of a stationary multivariate long memory process under fairly general conditions. In this theory the convergence rates and the limits (the fractional Brownian motion, the Rosenblatt process, etc.) all depend intrinsically on the degree of long memory in the process. The theory of the sample moments is then applied to the multiple linear regression model. An interesting finding is that, even though all the regressors and the disturbance are stationary and ergodic, the joint long memory in one single regressor and in the disturbance can invalidate the usual asymptotic theory for the ordinary least squares (OLS) estimation. Specifically, the convergence rates of the OLS estimators become slower, the limits are not normal, and the standard t- and F-tests all collapse.
    URI: http://idv.sinica.edu.tw/metrics/Pdf_Papers/Fclt.pdf
    www.cambridge.org/
    http://nthur.lib.nthu.edu.tw/dspace/handle/987654321/62092
    Appears in Collections:[計量財務金融學系] 期刊論文

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